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A Fitness Landscape Analysis of Pareto Local Search on Bi-objective Permutation Flowshop Scheduling Problems

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Evolutionary Multi-Criterion Optimization (EMO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10173))

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Abstract

We study the difficulty of solving different bi-objective formulations of the permutation flowshop scheduling problem by adopting a fitness landscape analysis perspective. Our main goal is to shed the light on how different problem features can impact the performance of Pareto local search algorithms. Specifically, we conduct an empirical analysis addressing the challenging question of quantifying the individual effect and the joint impact of different problem features on the success rate of the considered approaches. Our findings support that multi-objective fitness landscapes enable to devise sound general-purpose features for assessing the expected difficulty in solving permutation flowshop scheduling problems, hence pushing a step towards a better understanding of the challenges that multi-objective randomized search heuristics have to face.

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References

  1. Aguirre, H., Tanaka, K.: Working principles, behavior, and performance of MOEAs on MNK-landscapes. Eur. J. Oper. Res. 181(3), 1670–1690 (2007)

    Article  MATH  Google Scholar 

  2. Basseur, M., Liefooghe, A.: Metaheuristics for biobjective flow shop scheduling. In: Metaheuristics for Production Scheduling, Chap. 9, pp. 225–252. Wiley (2013)

    Google Scholar 

  3. Basseur, M., Seynhaeve, F., Talbi, E.G.: Design of multi-objective evolutionary algorithms: application to the flow shop scheduling problem. In: CEC 2002, NJ, USA, pp. 1151–1156 (2002)

    Google Scholar 

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  5. Daolio, F., Liefooghe, A., Verel, S., Aguirre, H., Tanaka, K.: Problem features vs. algorithm performance on rugged multi-objective combinatorial fitness landscapes. Evol. Comput. (to appear)

    Google Scholar 

  6. Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems. Comput. Oper. Res. 38(8), 1219–1236 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  7. Ehrgott, M.: Multicriteria Optimization. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  8. Geiger, M.: On operators and search space topology in multi-objective flow shop scheduling. Eur. J. Oper. Res. 181(1), 195–206 (2007)

    Article  MATH  Google Scholar 

  9. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston (1989)

    MATH  Google Scholar 

  10. Hoos, H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2004)

    MATH  Google Scholar 

  11. Liefooghe, A., Basseur, M., Jourdan, L., Talbi, E.-G.: Combinatorial optimization of stochastic multi-objective problems: an application to the flow-shop scheduling problem. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 457–471. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  12. Minella, G., Ruiz, R., Ciavotta, M.: A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. INFORMS J. Comput. 20(3), 451–471 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  13. Paquete, L., Schiavinotto, T., Stützle, T.: On local optima in multiobjective combinatorial optimization problems. Ann. Oper. Res. 156(1), 83–97 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  14. Richter, H., Engelbrecht, A. (eds.): Recent Advances in the Theory and Application of Fitness Landscapes. Springer, Heidelberg (2014)

    Google Scholar 

  15. Taillard, E.D.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64, 278–285 (1993)

    Article  MATH  Google Scholar 

  16. T’Kindt, V., Billaut, J.C.: Multicriteria Scheduling: Theory, Models and Algorithms. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  17. Verel, S., Liefooghe, A., Jourdan, L., Dhaenens, C.: On the structure of multiobjective combinatorial search space: MNK-landscapes with correlated objectives. Eur. J. Oper. Res. 227(2), 331–342 (2013)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, 59000, Lille, France

    Arnaud Liefooghe & Bilel Derbel

  2. Inria Lille – Nord Europe, 59650, Villeneuve d’Ascq, France

    Arnaud Liefooghe & Bilel Derbel

  3. Univ. Littoral Côte d’Opale, LISIC, 62100, Calais, France

    Sébastien Verel

  4. Faculty of Engineering, Shinshu University, Nagano, Japan

    Hernán Aguirre & Kiyoshi Tanaka

Authors
  1. Arnaud Liefooghe
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  2. Bilel Derbel
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  3. Sébastien Verel
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  4. Hernán Aguirre
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  5. Kiyoshi Tanaka
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Corresponding author

Correspondence to Arnaud Liefooghe .

Editor information

Editors and Affiliations

  1. University of Münster, Münster, Germany

    Heike Trautmann

  2. TU Dortmund University, Dortmund, Germany

    Günter Rudolph

  3. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  4. CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  5. Clemson University, Clemson, South Carolina, USA

    Margaret Wiecek

  6. University of Surrey, Guildford, United Kingdom

    Yaochu Jin

  7. University of Münster, Münster, Germany

    Christian Grimme

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Liefooghe, A., Derbel, B., Verel, S., Aguirre, H., Tanaka, K. (2017). A Fitness Landscape Analysis of Pareto Local Search on Bi-objective Permutation Flowshop Scheduling Problems. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_29

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  • DOI: https://doi.org/10.1007/978-3-319-54157-0_29

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-54157-0

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